Object/target recognition in aerial videos has been a problem in machine vision research for many years. A traditional approach to object recognition in aerial videos involves detecting the moving targets, tracking them, and then applying a recognition algorithm on the target image ROI (region of interest). This approach suffers from the fact that only moving, targets can be detected and recognized. Furthermore, target detection can be overwhelmed by clutters caused by apparent motion due to platform movement.
As noted above, most researchers follow the traditional paradigm of detection-tracking-recognition. In a separate art, bio-inspired attention and recognition algorithms are a new breed of algorithms that have attracted significant attention in recent years due to their simplicity and performance. However, application of this approach has not been demonstrated before for aerial video target detection/recognition due to the immaturity of the algorithms. Further, such systems do not inherently account for platform motion which is an important factor in aerial videos.
Thus, a continuing need exists for an aerial video target detection system that employs bio-inspired algorithms and that is capable of handling the apparent motion due to platform movement.